Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches
By a News Reporter-Staff News Editor at Journal of Engineering -- New research on Information Technology - Data Mining is the subject of a report. According to news reporting from Hubei, People’s Republic of China, by VerticalNews journalists, research stated, “This paper depicted the novel data mining based methods that consist of six models for predicting accurate future heating and cooling load demand of water source heat pump, with the objective of enhancing the prediction accuracy and the management of future load. The proposed model was developed to ease generalization to other buildings, by making use of readily available measurements of a comparatively small number of variables related to water source heat pump operation in the building environment.”
Financial support for this research came from National Natural Science Foundation of China.
The news correspondents obtained a quote from the research from the Huazhong University of Science and Technology, “The six models are - tree bagger, Gaussian process regression, multiple linear regression, bagged tree, boosted tree and neural network. The input parameter comprised the prescribed period, external climate data and the diverse load conditions of water source heat pump. The output was electrical power consumption of water source heat pump. In this study, simulations were conducted in three sessions - 7-day, 14-day and 1-month from 8th July to 7th August 2016. The forecast precisions of data mining models were measured by diverse indices. The performance indices which were used in assessing the prediction performance were - mean absolute error, coefficient of correlation, coefficient of variation, root mean square error, mean square error and mean absolute percentage error. The mean absolute percentage error results for 7-day future energy demand forecasting from tree bagger, Gaussian process regression, bagged tree, boosted tree, neural network and multiple linear regression were 3.544%, 0.405%, 1.703%, 1.928%, 2.592% and 13.053%, respectively. Moreover, when the proposed data mining model performance was compared with the existing studies, the mean absolute percentage error of 2.515% was found out for the first session, 7-day.”
According to the news reporters, the research concluded: “The results also showed that the six models were efficient in foreseeing the abnormal behavior and future cooling and heating load demand in the building environment.”
For more information on this research see: Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches. Energy and Buildings , 2018;166():460-476. Energy and Buildings can be contacted at: Elsevier Science Sa, PO Box 564, 1001 Lausanne, Switzerland. (Elsevier - www.elsevier.com; Energy and Buildings - http://www.journals.elsevier.com/energy-and-buildings/)
Our news journalists report that additional information may be obtained by contacting T. Ahmad, Huazhong University of Science & Technology, Sch Energy & Power Engn, Wuhan, Hubei, People’s Republic of China.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.enbuild.2018.01.066. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-05-07), New Data Mining Findings Reported from Huazhong University of Science and Technology (Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches), Journal of Engineering, 936, ISSN: 1945-872X, BUTTER® ID: 015627394
From the newsletter Journal of Engineering.
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